LBNP: Learning features between neighboring points for point cloud classification
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| Опубликовано в:: | PLoS One vol. 20, no. 1 (Jan 2025), p. e0314086 |
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| Главный автор: | |
| Другие авторы: | , , , , , , |
| Опубликовано: |
Public Library of Science
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| Предметы: | |
| Online-ссылка: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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| 024 | 7 | |a 10.1371/journal.pone.0314086 |2 doi | |
| 035 | |a 3152099281 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Wang, Lei | |
| 245 | 1 | |a LBNP: Learning features between neighboring points for point cloud classification | |
| 260 | |b Public Library of Science |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Inspired by classical works, when constructing local relationships in point clouds, there is always a geometric description of the central point and its neighboring points. However, the basic geometric representation of the central point and its neighborhood is insufficient. Drawing inspiration from local binary pattern algorithms used in image processing, we propose a novel method for representing point cloud neighborhoods, which we call Point Cloud Local Auxiliary Block (PLAB). This module explores useful neighborhood features by learning the relationships between neighboring points, thereby enhancing the learning capability of the model. In addition, we propose a pure Transformer structure that takes into account both local and global features, called Dual Attention Layer (DAL), which enables the network to learn valuable global features as well as local features in the aggregated feature space. Experimental results show that our method performs well on both coarse- and fine-grained point cloud datasets. We will publish the code and all experimental training logs on GitHub. | |
| 653 | |a Algorithms | ||
| 653 | |a Image processing | ||
| 653 | |a Learning | ||
| 653 | |a Cloud classification | ||
| 653 | |a Methods | ||
| 653 | |a Deep learning | ||
| 653 | |a Computer vision | ||
| 653 | |a Neighborhoods | ||
| 653 | |a Neural networks | ||
| 653 | |a Classification | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Huang, Ming | |
| 700 | 1 | |a Yang, Zhenqing | |
| 700 | 1 | |a Wu, Rui | |
| 700 | 1 | |a Qiu, Dashi | |
| 700 | 1 | |a Xiao, Xingxing | |
| 700 | 1 | |a Li, Dong | |
| 700 | 1 | |a Chen, Cai | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 1 (Jan 2025), p. e0314086 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3152099281/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3152099281/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3152099281/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |